Convolutional Neural Networks (CNNs) are like a highly specialized team of experts working together to solve a complex problem, similar to how different specialists might collaborate to diagnose and treat a medical condition.
Imagine you have a team of doctors each with their own area of expertise—one for heart issues, another for skin conditions, and another for neurological problems. Instead of one doctor trying to handle everything, each specialist focuses on what they know best, and together they provide a comprehensive diagnosis.
CNNs work in a similar way but for images. Instead of a team of doctors, you have a network of “neurons” or processing units that each focus on different parts of an image. Some might look at edges, others at colors, and others at shapes. These specialized units work together to recognize patterns and details in the image, such as identifying a cat versus a dog.
For example, when a CNN is trained to recognize faces, it first looks at small features like eyes and noses, then combines these features to identify and distinguish between different faces. Over time, and with lots of examples, the CNN gets better at recognizing these patterns and can accurately identify faces in new images.
In simple terms, a Convolutional Neural Network is a type of computer program designed to recognize patterns in images by using specialized units that work together, similar to how a team of experts would collaborate to diagnose and solve a complex problem.
